Báo cáo khoa học: "A Large-Scale Unified Lexical-Semantic Resource Based on LMF" docx

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 580–590,
Avignon, France, April 23 - 27 2012.
c
2012 Association for Computational Linguistics
UBY – A Large-Scale Unified Lexical-Semantic Resource
Based on LMF
Iryna Gurevych
†‡
, Judith Eckle-Kohler
‡
, Silvana Hartmann
‡
, Michael Matuschek
‡
,
Christian M. Meyer
‡
and Christian Wirth
‡
† Ubiquitous Knowledge Processing Lab (UKP-DIPF)
German Institute for Educational Research and Educational Information
‡ Ubiquitous Knowledge Processing Lab (UKP-TUDA)
Department of Computer Science
Technische Universit
¨
at Darmstadt

Abstract
We present UBY, a large-scale lexical-
semantic resource combining a wide range
of information from expert-constructed

and collaboratively constructed resources
for English and German. It currently
contains nine resources in two lan-
guages: English WordNet, Wiktionary,
Wikipedia, FrameNet and VerbNet,
German Wikipedia, Wiktionary and
GermaNet, and multilingual OmegaWiki
modeled according to the LMF standard.
For FrameNet, VerbNet and all collabora-
tively constructed resources, this is done
for the first time. Our LMF model captures
lexical information at a fine-grained level
by employing a large number of Data
Categories from ISOCat and is designed
to be directly extensible by new languages
and resources. All resources in UBY can
be accessed with an easy to use publicly
available API.
1 Introduction
Lexical-semantic resources (LSRs) are the foun-
dation of many NLP tasks such as word sense
disambiguation, semantic role labeling, question
answering and information extraction. They are
needed on a large scale in different languages.
The growing demand for resources is met nei-
ther by the largest single expert-constructed re-
sources (ECRs), such as WordNet and FrameNet,
whose coverage is limited, nor by collaboratively
constructed resources (CCRs), such as Wikipedia
and Wiktionary, which encode lexical-semantic

knowledge in a less systematic form than ECRs,
because they are lacking expert supervision.
Previously, there have been several indepen-
dent efforts of combining existing LSRs to en-
hance their coverage w.r.t. their breadth and depth,
i.e. (i) the number of lexical items, and (ii) the
types of lexical-semantic information contained
(Shi and Mihalcea, 2005; Johansson and Nugues,
2007; Navigli and Ponzetto, 2010b; Meyer and
Gurevych, 2011). As these efforts often targeted
particular applications, they focused on aligning
selected, specialized information types. To our
knowledge, no single work focused on modeling
a wide range of ECRs and CCRs in multiple lan-
guages and a large variety of information types in
a standardized format. Frequently, the presented
model is not easily scalable to accommodate an
open set of LSRs in multiple languages and the in-
formation mined automatically from corpora. The
previous work also lacked the aspects of lexicon
format standardization and API access. We be-
lieve that easy access to information in LSRs is
crucial in terms of their acceptance and broad ap-
plicability in NLP.
In this paper, we propose a solution to this. We
define a standardized format for modeling LSRs.
This is a prerequisite for resource interoperabil-
ity and the smooth integration of resources. We
employ the ISO standard Lexical Markup Frame-
work (LMF: ISO 24613:2008), a metamodel for

LSRs (Francopoulo et al., 2006), and Data Cate-
gories (DCs) selected from ISOCat.
1
One of the
main challenges of our work is to develop a model
that is standard-compliant, yet able to express the
information contained in diverse LSRs, and that in
the long term supports the integration of the vari-
ous resources.
The main contributions of this paper can be
1
/>580
summarized as follows: (1) We present an LMF-
based model for large-scale multilingual LSRs
called UBY-LMF. We model the lexical-semantic
information down to a fine-grained level of in-
formation (e.g. syntactic frames) and employ
standardized definitions of linguistic information
types from ISOCat. (2) We present UBY, a large-
scale LSR implementing the UBY-LMF model.
UBY currently contains nine resources in two
languages: English WordNet (WN, Fellbaum
(1998), Wiktionary
2
(WKT-en), Wikipedia
3
(WP-
en), FrameNet (FN, Baker et al. (1998)), and
VerbNet (VN, Kipper et al. (2008)); German Wik-
tionary (WKT-de), Wikipedia (WP-de), and Ger-

maNet (GN, Kunze and Lemnitzer (2002)), and
the English and German entries of OmegaWiki
4
(OW), referred to as OW-en and OW-de. OW,
a novel CCR, is inherently multilingual – its ba-
sic structure are multilingual synsets, which are a
valuable addition to our multilingual UBY. Essen-
tial to UBY are the nine pairwise sense alignments
between resources, which we provide to enable
resource interoperability on the sense level, e.g.
by providing access to the often complementary
information for a sense in different resources. (3)
We present a Java-API which offers unified access
to the information contained in UBY.
We will make the UBY-LMF model, the re-
source UBY and the API freely available to the
research community.
5
This will make it easy for
the NLP community to utilize UBY in a variety of
tasks in the future.
2 Related Work
The work presented in this paper concerns
standardization of LSRs, large-scale integration
thereof at the representational level, and the uni-
fied access to lexical-semantic information in the
integrated resources.
Standardization of resources. Previous work
includes models for representing lexical informa-
tion relative to ontologies (Buitelaar et al., 2009;

McCrae et al., 2011), and standardized single
wordnets (English, German and Italian wordnets)
in the ISO standard LMF (Soria et al., 2009; Hen-
rich and Hinrichs, 2010; Toral et al., 2010).
2
/>3
/>4
/>5
/>McCrae et al. (2011) propose LEMON, a con-
ceptual model for lexicalizing ontologies as an
extension of the LexInfo model (Buitelaar et al.,
2009). LEMON provides an LMF-implementation
in the Web Ontology Language (OWL), which
is similar to UBY-LMF, as it also uses DCs
from ISOCat, but diverges further from the stan-
dard (e.g. by removing structural elements such
as the predicative representation class). While
we focus on modeling lexical-semantic informa-
tion comprehensively and at a fine-grained level,
the goal of LEMON is to support the linking be-
tween ontologies and lexicons. This goal entails
a task-targeted application: domain-specific lex-
icons are extracted from ontology specifications
and merged with existing LSRs on demand. As a
consequence, there is no available large-scale in-
stance of the LEMON model.
Soria et al. (2009) define WordNet-LMF, an
LMF model for representing wordnets used in
the KYOTO project, and Henrich and Hinrichs
(2010) do this for GN, the German wordnet.

These models are similar, but they still present
different implementations of the LMF meta-
model, which hampers interoperability between
the resources. We build upon this work, but ex-
tend it significantly: UBY goes beyond model-
ing a single ECR and represents a large number
of both ECRs and CCRs with very heterogeneous
content in the same format. Also, UBY-LMF
features deeper modeling of lexical-semantic in-
formation. Henrich and Hinrichs (2010), for
instance, do not explicitly model the argument
structure of subcategorization frames, since each
frame is represented as a string. In UBY-LMF,
we represent them at a fine-grained level neces-
sary for the transparent modeling of the syntax-
semantics interface.
Large-scale integration of resources. Most
previous research efforts on the integration of re-
sources targeted at world knowledge rather than
lexical-semantic knowledge. Well known exam-
ples are YAGO (Suchanek et al., 2007), or DBPe-
dia (Bizer et al., 2009).
Atserias et al. (2004) present the Meaning Mul-
tilingual Central Repository (MCR). MCR inte-
grates five local wordnets based on the Interlin-
gual Index of EuroWordNet (Vossen, 1998). The
overall goal of the work is to improve word sense
disambiguation. This work is similar to ours, as it
581
aims at a large-scale multilingual resource and in-

cludes several resources. It is however restricted
to a single type of resource (wordnets) and fea-
tures a single type of lexical information (seman-
tic relations) specified upon synsets. Similarly,
de Melo and Weikum (2009) create a multilin-
gual wordnet by integrating wordnets, bilingual
dictionaries and information from parallel cor-
pora. None of these resources integrate lexical-
semantic information, such as syntactic subcate-
gorization or semantic roles.
McFate and Forbus (2011) present NULEX,
a syntactic lexicon automatically compiled from
WN, WKT-en and VN. As their goal is to cre-
ate an open-license resource to enhance syntactic
parsing, they enrich verbs and nouns in WN with
inflection information from WKT-en and syntac-
tic frames from VN. Thus, they only use a small
part of the lexical information present in WKT-en.
Padr
´
o et al. (2011) present their work on lex-
icon merging within the Panacea Project. One
goal of Panacea is to create a lexical resource de-
velopment platform that supports large-scale lex-
ical acquisition and can be used to combine exist-
ing lexicons with automatically acquired ones. To
this end, Padr
´
o et al. (2011) explore the automatic
integration of subcategorization lexicons. Their

current work only covers Spanish, and though
they mention the LMF standard as a potential data
model, they do not make use of it.
Shi and Mihalcea (2005) integrate FN, VN and
WN, and Palmer (2009) presents a combination of
Propbank, VN and FN in a resource called SEM-
LINK in order to enhance semantic role labeling.
Similar to our work, multiple resources are in-
tegrated, but their work is restricted to a single
language and does not cover CCRs, whose pop-
ularity and importance has grown tremendously
over the past years. In fact, with the excep-
tion of NULEX, CCRs have only been consid-
ered in the sense alignment of individual resource
pairs (Navigli and Ponzetto, 2010a; Meyer and
Gurevych, 2011).
API access for resources. An important factor
to the success of a large, integrated resource is a
single public API, which facilitates the access to
the information contained in the resource. The
most important LSRs so far can be accessed us-
ing various APIs, for instance the Java WordNet
API,
6
or the Java-based Wikipedia API.
7
With a stronger focus of the NLP community
on sharing data and reproducing experimental re-
sults these tools are becoming important as never
before. Therefore, a major design objective of

UBY is a single API. This is similar in spirit to the
motivation of Pradhan et al. (2007), who present
integrated access to corpus annotations as a main
goal of their work on standardizing and integrat-
ing corpus annotations in the OntoNotes project.
To summarize, related work focuses either on
the standardization of single resources (or a single
type of resource), which leads to several slightly
different formats constrained to these resources,
or on the integration of several resources in an
idiosyncratic format. CCRs have not been con-
sidered at all in previous work on resource stan-
dardization, and the level of detail of the model-
ing is insufficient to fully accommodate different
types of lexical-semantic information. API ac-
cess is rarely provided. This makes it hard for
the community to exploit their results on a large
scale. Thus, it diminishes the impact that these
projects might achieve upon NLP beyond their
original specific purpose, if their results were rep-
resented in a unified resource and could easily be
accessed by the community through a single pub-
lic API.
3 UBY – Data model
LMF defines a metamodel of LSRs in the Uni-
fied Modeling Language (UML). It provides a
number of UML packages and classes for model-
ing many different types of resources, e.g. word-
nets and multilingual lexicons. The design of
a standard-compliant lexicon model in LMF in-

volves two steps: in the first step, the structure
of the lexicon model has to be defined by choos-
ing a combination of the LMF core package and
zero to many extensions (i.e. UML packages). In
the second step, these UML classes are enriched
by attributes. To contribute to semantic interop-
erability, it is essential for the lexicon model that
the attributes and their values refer to Data Cat-
egories (DCs) taken from a reference repository.
DCs are standardized specifications of the terms
that are used for attributes and their values, or in
other words, the linguistic vocabulary occurring
6
/>7
/>582
in a lexicon model. Consider, for instance, the
term lexeme that is defined differently in WN and
FN: in FN, a lexeme refers to a word form, not
including the sense aspect. In WN, on the con-
trary, a lexeme is an abstract pairing of mean-
ing and form. According to LMF, the DCs are
to be selected from ISOCat, the implementation
of the ISO 12620 Data Category Registry (DCR,
Broeder et al. (2010)), resulting in a Data Cate-
gory Selection (DCS).
Design of UBY-LMF. We have designed UBY-
LMF
8
as a model of the union of various hetero-
geneous resources, namely WN, GN, FN, and VN

on the one hand and CCRs on the other hand.
Two design principles guided our development
of UBY-LMF: first, to preserve the information
available in the original resources and to uni-
formly represent it in UBY-LMF. Second, to be
able to extend UBY in the future by further lan-
guages, resources, and types of linguistic infor-
mation, in particular, alignments between differ-
ent LSRs.
Wordnets, FN and VN are largely complemen-
tary regarding the information types they provide,
see, e.g. Baker and Fellbaum (2009). Accord-
ingly, they use different organizational units to
represent this information. Wordnets, such as
WN and GN, primarily contain information on
lexical-semantic relations, such as synonymy, and
use synsets (groups of lexemes that are synony-
mous) as organizational units. FN focuses on
groups of lexemes that evoke the same prototypi-
cal situation (so-called semantic frames, Fillmore
(1982)) involving semantic roles (so-called frame
elements). VN, a large-scale verb lexicon, is or-
ganized in Levin-style verb classes (Levin, 1993)
(groups of verbs that share the same syntactic al-
ternations and semantic roles) and provides rich
subcategorization frames including semantic roles
and a specification of semantic predicates.
UBY-LMF employs several direct subclasses
of Lexicon in order to account for the various or-
ganization types found in the different LSRs con-

sidered. While the LexicalEntry class reflects
the traditional headword-based lexicon organiza-
tion, Synset represents synsets from wordnets,
SemanticPredicate models FN semantic
frames, and SubcategorizationFrameSet
corresponds to VN alternation classes.
8
See www.ukp.tu-darmstadt.de/data/uby
SubcategorizationFrame is com-
posed of syntactic arguments, while
SemanticPredicate is composed of se-
mantic arguments. The linking between syntactic
and semantic arguments is represented by the
SynSemCorrespondence class.
The SenseAxis class is very important in
UBY-LMF, as it connects the different source
LSRs. Its role is twofold: first, it links the cor-
responding word senses from different languages,
e.g. English and German. Second, it represents
monolingual sense alignments, i.e. sense align-
ments between different lexicons in the same lan-
guage. The latter is a novel interpretation of
SenseAxis introduced by UBY-LMF.
The organization of lexical-semantic knowl-
edge found in WP, WKT, and OW can be mod-
eled with the classes in UBY-LMF as well. WP
primarily provides encyclopedic information on
nouns. It mainly consists of article pages which
are modeled as Senses in UBY-LMF.
WKT is in many ways similar to tradi-

tional dictionaries, because it enumerates senses
under a given headword on an entry page.
Thus, WKT entry pages can be represented by
LexicalEntries and WKT senses by Senses.
OW is different from WKT and WP, as it is or-
ganized in multilingual synsets. To model OW
in UBY-LMF, we split the synsets per language
and included them as monolingual Synsets in
the corresponding Lexicon (e.g., OW-en or OW-
de). The original multilingual information is pre-
served by adding a SenseAxis between corre-
sponding synsets in OW-en and OW-de.
The LMF standard itself contains only few lin-
guistic terms and does neither specify attributes
nor their values. Therefore, an important task in
developing UBY-LMF has been the specification
of attributes and their values along with the proper
attachment of attributes to LMF classes. In partic-
ular, this task involved selecting DCs from ISO-
Cat and, if necessary, adding new DCs to ISOCat.
Extensions in UBY-LMF. Although UBY-
LMF is largely compliant with LMF, the task of
building a homogeneous lexicon model for many
highly heterogeneous LSRs led us to extend LMF
in several ways: we added two new classes and
several new relationships between classes.
First, we were facing a huge variety of lexical-
semantic labels for many different dimensions of
583
semantic classification. Examples of such dimen-

sions include ontological type (e.g. selectional re-
strictions in VN and FN), domain (e.g. Biology in
WN), style and register (e.g. labels in WKT, OW),
or sentiment (e.g. sentiment of lexical units in
FN). Since we aim at an extensible LMF-model,
capable of representing further dimensions of se-
mantic classification, we did not squeeze the in-
formation on semantic classes present in the con-
sidered LSRs into existing LMF classes. Instead,
we addressed this issue by introducing a more
general class, SemanticLabel, which is an op-
tional subclass of Sense, SemanticPredicate,
and SemanticArgument. This new class has
three attributes, encoding the name of the label,
its type (e.g. ontological, register, sentiment), and
a numeric quantification (e.g. sentiment strength).
Second, we attached the subclass Frequency
to most of the classes in UBY-LMF, in order to
encode frequency information. This is of partic-
ular importance when using the resource in ma-
chine learning applications. This extension of the
standard has already been made in WordNet-LMF
(Soria et al., 2009). Currently, the Frequency
class is used to keep corpus frequencies for lex-
ical units in FN, but we plan to use it for en-
riching many other classes with frequency in-
formation in future work, such as Senses or
SubcategorizationFrames.
Third, the representation of FN in LMF re-
quired adding two new relationships between

LMF classes: we added a relationship between
SemanticArgument and Definition, in or-
der to represent the definitions available for frame
elements in FN. In addition, we added a re-
lationship between the Context class and the
MonoLingualExternalRef, to represent the
links to annotated corpus sentences in FN.
Finally, WKT turned out to be hard to tackle,
because it contains a special kind of ambiguity in
the semantic relations and translation links listed
for senses: the targets of both relations and trans-
lation links are ambiguous, as they refer to lem-
mas (word forms), rather than to senses (Meyer
and Gurevych, 2010). These ambiguous rela-
tion targets could not directly be represented in
LMF, since sense and translation relations are
defined between senses. To resolve this, we
added a relationship between SenseRelation
and FormRepresentation, in order to encode
the ambiguous WKT relation target as a word
form. Disambiguating the WKT relation targets
to infer the target sense is left to future work.
A related issue occurred, when we mapped WN
to LMF. WN encodes morphologically related
forms as sense relations. UBY-LMF represents
these related forms not only as sense relations (as
in WordNet-LMF), but also at the morphologi-
cal level using the RelatedForm class from the
LMF Morphology extension. In LMF, however,
the RelatedForm class for morphologically re-

lated lexemes is not associated with the corre-
sponding sense in any way. Discarding the WN
information on the senses involved in a particular
morphological relation would lead to information
loss in some cases. Consider as an example the
WN verb buy (purchase) which is derivationally
related to the noun buy, while on the other hand
buy (accept as true, e.g. I can’t buy this story) is
not derivationally related to the noun buy. We ad-
dressed this issue by adding a sense attribute to
the RelatedForm class. Thus, in extension of
LMF, UBY-LMF allows sense relations to refer to
a form relation target and morphological relations
to refer to a sense relation target.
Data Categories in UBY-LMF. We encoun-
tered large differences in the availability of DCs
in ISOCat for the morpho-syntactic, lexical-
syntactic, and lexical-semantic parts of UBY-
LMF. Many DCs were missing in ISOCat and we
had to enter them ourselves. While this was feasi-
ble at the morpho-syntactic and lexical-syntactic
level, due to a large body of standardization re-
sults available, it was much harder at the lexical-
semantic level where standardization is still on-
going. At the lexical-semantic level, UBY-LMF
currently allows string values for a number of at-
tribute values, e.g. for semantic roles. We can eas-
ily integrate the results of the ongoing standard-
ization efforts into UBY-LMF in the future.
4 UBY – Population with information

4.1 Representing LSRs in UBY-LMF
UBY-LMF is represented by a DTD (as suggested
by the standard) which can be used to automat-
ically convert any given resource into the corre-
sponding XML format.
9
This conversion requires
a detailed analysis of the resource to be converted,
followed by the definition of a mapping of the
9
Therefore, UBY-LMF can be considered as a serializa-
tion of LMF.
584
concepts and terms used in the original resource
to the UBY-LMF model. There are two major
tasks involved in the development of an automatic
conversion routine: first, the basic organizational
unit in the source LSR has to be identified and
mapped, e.g. synset in WN or semantic frame in
FN, and second, it has to be determined, how a
(LMF) sense is defined in the source LSR.
A notable aspect of converting resources into
UBY-LMF is the harmonization of linguistic ter-
minology used in the LSRs. For instance, a
WN Word and a GN Lexical Unit are mapped to
Sense in UBY-LMF.
We developed reusable conversion routines for
the future import of updated versions of the source
LSRs into UBY, provided the structure of the
source LSR remains stable. These conversion

routines extract lexical data from the source LSRs
by calling their native APIs (rather than process-
ing the underlying XML data). Thus, all lexical
information which can be accessed via the APIs
is converted into UBY-LMF.
Converting the LSRs introduced in the previ-
ous section yielded an instantiation of UBY-LMF
named UBY. The LexicalResource instance
UBY currently comprises 10 Lexicon instances,
one each for OW-de and OW-en, and one lexicon
each for the remaining eight LSRs.
4.2 Adding Sense Alignments
Besides the uniform and standardized representa-
tion of the single LSRs, one major asset of UBY
is the semantic interoperability of resources at the
sense level. In the following, we (i) describe how
we converted already existing sense alignments of
resources into LMF, and (ii) present a framework
to infer alignments automatically for any pair of
resources.
Existing Alignments. Previous work on sense
alignment yielded several alignments, such as
WN–WP-en (Niemann and Gurevych, 2011),
WN–WKT-en (Meyer and Gurevych, 2011) and
VN–FN (Palmer, 2009).
We converted these alignments into UBY-LMF
by creating a SenseAxis instance for each pair of
aligned senses. This involved mapping the sense
IDs from the proprietary alignment files to the
corresponding sense IDs in UBY.

In addition, we integrated the sense alignments
already present in OW and WP. Some OW en-
tries provide links to the corresponding WP page.
Also, the German and English language editions
of WP and OW are connected by inter-language
links between articles (Senses in UBY). We can
expect that these links have high quality, as they
were entered manually by users and are subject
to community control. Therefore, we straightfor-
wardly imported them into UBY.
Alignment Framework. Automatically creat-
ing new alignments is difficult because of word
ambiguities, different granularities of senses,
or language specific conceptualizations (Navigli,
2006). To support this task for a large number
of resources across languages, we have designed
a flexible alignment framework based on the
state-of-the-art method of Niemann and Gurevych
(2011). The framework is generic in order to al-
low alignments between different kinds of entities
as found in different resources, e.g. WN synsets,
FN frames or WP articles. The only requirement
is that the individual entities are distinguishable
by a unique identifier in each resource.
The alignment consists of the following steps:
First, we extract the alignment candidates for a
given resource pair, e.g. WN sense candidates for
a WKT-en entry. Second, we create a gold stan-
dard by manually annotating a subset of candi-
date pairs as “valid“ or “non-valid“. Then, we

extract the sense representations (e.g. lemmatized
bag-of-words based on glosses) to compute the
similarity of word senses (e.g. by cosine similar-
ity). The gold standard with corresponding sim-
ilarity values is fed into Weka (Hall et al., 2009)
to train a machine learning classifier, and in the
final step this classifier is used to automatically
classify the candidate sense pairs as (non-)valid
alignment. Our framework also allows us to train
on a combination of different similarity measures.
Using our framework, we were able to re-
produce the results reported by Niemann and
Gurevych (2011) and Meyer and Gurevych
(2011) based on the publicly available evaluation
datasets
10
and the configuration details reported
in the corresponding papers.
Cross-Lingual Alignment. In order to align
word senses across languages, we extended the
monolingual sense alignment described above to
the cross-lingual setting. Our approach utilizes
10
/>585
Moses,
11
trained on the Europarl corpus. The
lemma of one of the two senses to be aligned
as well as its representations (e.g. the gloss) is
translated into the language of the other resource,

yielding a monolingual setting. E.g., the WN
synset {vessel, watercraft} with its gloss ’a craft
designed for water transportation’ is translated
into {Schiff, Wasserfahrzeug} and ’Ein Fahrzeug
f
¨
ur Wassertransport’, and then the candidate ex-
traction and all downstream steps can take place
in German. An inherent problem with this ap-
proach is that incorrect translations also lead to
invalid alignment candidates. However, these are
most probably filtered out by the machine learn-
ing classifier as the calculated similarity between
the sense representations (e.g. glosses) should be
low if the candidates do not match.
We evaluated our approach by creating a cross-
lingual alignment between WN and OW-de, i.e.
the concepts in OW with a German lexicaliza-
tion.
12
To our knowledge, this is the first study on
aligning OW with another LSR. OW is especially
interesting for this task due to its multilingual con-
cepts, as described by Matuschek and Gurevych
(2011). The created gold standard could, for in-
stance, be re-used to evaluate alignments for other
languages in OW.
To compute the similarity of word senses, we
followed the approach by Niemann and Gurevych
(2011) while covering both translation directions.

We used the cosine similarity for comparing the
German OW glosses with the German translations
of WN glosses and cosine and personalized page
rank (PPR) similarity for comparison of the Ger-
man OW glosses translated into English with the
original English WN glosses. Note that PPR sim-
ilarity is not available for German as it is based
on WN. Thereby, we filtered out the OW con-
cepts without a German gloss which left us with
11,806 unique candidate pairs. We randomly se-
lected 500 WN synsets for analysis yielding 703
candidate pairs. These were manually annotated
as being (non-)alignments. For the subsequent
machine learning task we used a simple threshold-
based classifier and ten-fold cross validation.
Table 1 summarizes the results of different sys-
tem configurations. We observe that translation
11
/>12
OmegaWiki consists of interlinked language-
independent concepts to which lexicalizations in several
languages are attached.
Translation Similarity
direction measure P R F
1
EN > DE Cosine (Cos) 0.666 0.575 0.594
DE > EN Cos 0.674 0.658 0.665
DE > EN PPR 0.721 0.712 0.716
DE > EN PPR + Cos 0.723 0.712 0.717
Table 1: Cross-lingual alignment results

into English works significantly better than into
German. Also, the more elaborate similarity mea-
sure PPR yields better results than cosine similar-
ity, while the best result is achieved by a combina-
tion of both. Niemann and Gurevych (2011) make
a similar observation for the monolingual setting.
Our F-measure of 0.717 in the best configuration
lies between the results of Meyer and Gurevych
(2011) (0.66) and Niemann and Gurevych (2011)
(0.78), and thus verifies the validity of the ma-
chine translation approach. Therefore, the best
alignment was subsequently integrated into UBY.
5 Evaluating UBY
We performed an intrinsic evaluation of UBY by
computing a number of resource statistics. Our
evaluation covers two aspects: first, it addresses
the question if our automatic conversion routines
work correctly. Second, it provides indicators for
assessing UBY in terms of the gain in coverage
compared to the single LSRs.
Correctness of conversion. Since we aim to
preserve the maximal amount of information from
the original LSRs, we should be able to replace
any of the original LSRs and APIs by UBY and
the UBY-API without losing information. As
the conversion is largely performed automatically,
systematic errors and information loss could be
introduced by a faulty conversion routine. In or-
der to detect such errors and to prove the correct-
ness of the automatic conversion and the result-

ing representation, we have compared the orig-
inal resource statistics of the classes and infor-
mation types in the source LSRs to the cor-
responding classes in their UBY counterparts.
For instance, the number of lexical relations in
WordNet has been compared to the number of
SenseRelations in the UBY WordNet lexi-
con.
13
13
For detailed analysis results see the UBY website.
586
Lexical Sense
Lexicon Entry Sense Relation
FN 9,704 11,942 –
GN 83,091 93,407 329,213
OW-de 30,967 34,691 60,054
OW-en
51,715 57,921 85,952
WP-de 790,430 838,428 571,286
WP-en 2,712,117 2,921,455 3,364,083
WKT-de 85,575 72,752 434,358
WKT-en 335,749 421,848 716,595
WN 156,584 206,978 8,559
VN 3,962 31,891 –
UBY 4,259,894 4,691,313 5,300,941
Table 2: UBY resource statistics (selected classes).
Lexicon pair Languages SenseAxis
WN–WP-en EN–EN 50,351
WN–WKT-en EN–EN 99,662

WN–VN EN–EN 40,716
FN–VN EN–EN 17,529
WP-en–OW-en EN–EN 3,960
WP-de–OW-de DE–DE 1,097
WN–OW-de EN–DE 23,024
WP-en–WP-de EN–DE 463,311
OW-en–OW-de EN–DE 58,785
UBY All 758,435
Table 3: UBY alignment statistics.
Gain in coverage. UBY offers an increased
coverage compared to the single LSRs as reflected
in the resource statistics. Tables 2 and 3 show the
statistics on central classes in UBY. As UBY is
organized in several Lexicons, the number of
UBY lexical entries is the sum of the lexical en-
tries in all 10 Lexicons. Thus, UBY contains
more than 4.2 million lexical entries, 4.6 million
senses, 5.3 million semantic relations between
senses and more than 750,000 alignments. These
statistics represent the total numbers of lexical en-
tries, senses and sense relations in UBY without
filtering of identical (i.e. corresponding) lexical
entries, senses and relations. Listing the num-
ber of unique senses would require a full align-
ment between all integrated resources, which is
currently not available.
We can, however, show that UBY contains over
3.08 million unique lemma-POS combinations for
English and over 860,000 for German, over 3.94
million in total, see Table 4. Therefore, we as-

sessed the coverage on lemma level. Table 4 also
shows the number of lemmas with entries in one
or more than one lexicon, additionally split by
POS and language. Lemmas occurring only once
in UBY increase the coverage at lemma level. For
lemmas with parallel entries in several UBY lex-
icons, new information becomes available in the
form of additional sense definitions and comple-
mentary information types attached to lemmas.
Finally, the increase in coverage at sense level
can be estimated for senses that are aligned across
at least two UBY-lexicons. We gain access to
all available, partly complementary information
types attached to these aligned senses, e.g. seman-
tic relations, subcategorization frames, encyclo-
pedic or multilingual information. The number
of pairwise sense alignments provided by UBY is
given in Table 3. In addition, we computed how
many senses simultaneously take part in at least
two pairwise sense alignments. For English, this
applies to 31,786 senses, for which information
from 3 UBY lexicons is available.
EN Lexicons noun verb adjective
5 1 699 -
4 1,630 1,888 430
3 8,439 1,948 2,271
2 53,856 4,727 12,290
1 2,900,652 50,209 41,731
Σ (unique EN) 3,080,771
DE Lexicons noun verb adjective

4 1,546 - -
3 10,374 372 342
2 26,813 3,174 2,643
1 803,770 6,108 7,737
Σ (unique DE) 862,879
Table 4: Number of lemmas (split by POS and lan-
guage) with entries in i UBY lexicons, i = 1, . . . , 5.
6 Using UBY
UBY API. For convenient access to UBY, we
implemented a Java-API which is built around
the Hibernate
14
framework. Hibernate allows to
easily store the XML data which results from
converting resources into Uby-LMF into a corre-
sponding SQL database.
Our main design principle was to keep the ac-
cess to the resource as simple as possible, despite
the rich and complex structure of UBY. Another
14
/>587
important design aspect was to ensure that the
functionality of the individual, resource-specific
APIs or user interfaces is mirrored in the UBY
API. This enables porting legacy applications to
our new resource. To facilitate the transition to
UBY, we plan to provide reference tables which
list the corresponding UBY-API operations for the
most important operations in the WN API, some
of which are shown in Table 5.

WN function UBY function
Dictionary UBY
getIndexWord(pos,
lemma)
getLexicalEntries(
pos, lemma)
IndexWord LexicalEntry
getLemma() getLemmaForm()
Synset Synset
getGloss() getDefinitionText()
getWords() getSenses()
Pointer SynsetRelation
getType() getRelName()
Word Sense
getPointers() getSenseRelations()
Table 5: Some equivalent operations in WN API and
UBY API.
While it is possible to limit access to single re-
sources by a parameter and thus mimic the behav-
ior of the legacy APIs (e.g. only retrieve Synsets
and their relations from WN), the true power of
UBY API becomes visible when no such con-
straints are applied. In this case, all imported re-
sources are queried to get one combined result,
while retaining the source of the respective in-
formation. On top of this, the information about
existing sense alignments across resources can be
accessed via SenseAxis relations, so that the re-
turned combined result covers not only the lexi-
cal, but also the sense level.

Community issues. One of the most important
reasons for UBY is creating an easy-to-use pow-
erful LSR to advance NLP research and develop-
ment. Therefore, community building around the
resource is one of our major concerns. To this end,
we will offer free downloads of the lexical data
and software presented in this paper under open li-
censes, namely: The UBY-LMF DTD, mappings
and conversion tools for existing resources and
sense alignments, the Java API, and, as far as li-
censing allows,
15
already converted resources. If
resources cannot be made available for download,
the conversion tools will still allow users with ac-
cess to these resources to import them into UBY
easily. In this way, it will be possible for users to
build their “custom UBY” containing selected re-
sources. As the underlying resources are subject
to continuous change, updates of the correspond-
ing components will be made available on a regu-
lar basis.
7 Conclusions
We presented UBY, a large-scale, standardized
LSR containing nine widely used resources in two
languages: English WN, WKT-en, WP-en, FN
and VN, German WP-de, WKT-de, and GN, and
OW in English and German. As all resources
are modeled in UBY-LMF, UBY enables struc-
tural interoperability across resources and lan-

guages down to a fine-grained level of informa-
tion. For FN, VN and all of the CCRs in En-
glish and German, this is done for the first time.
Besides, by integrating sense alignments we also
enable the lexical-semantic interoperability of re-
sources. We presented a unified framework for
aligning any LSRs pairwise and reported on ex-
periments which align OW-de and WN. We will
release the UBY-LMF model, the resource and the
UBY-API at the time of publication.
16
Due to the
added value and the large scale of UBY, as well as
its ease of use, we believe UBY will boost the per-
formance of NLP making use of lexical-semantic
knowledge.
Acknowledgments
This work has been supported by the Emmy
Noether Program of the German Research Foun-
dation (DFG) under grant No. GU 798/3-1 and
by the Volkswagen Foundation as part of the
Lichtenberg-Professorship Program under grant
No. I/82806. We thank Richard Eckart de
Castilho, Yevgen Chebotar, Zijad Maksuti and Tri
Duc Nghiem for their contributions to this project.
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